Joint analysis of galaxy clustering and weak lensing via simulation-based inference

物理宇宙学由一个成功的标准模型主导，该模型将高能物理学与观测天文学联系起来，其中宇宙从“大爆炸”演变到现在的状态。然而，我们目前还不知道加速膨胀的原因和中微子粒子的质量。对膨胀加速度的精确测量将检验广义相对论的有效性，并揭示暗能量的物理性质。暗能量是一种未知的能量形式，在最大尺度上影响着宇宙。测量中微子的未知质量，长期以来被认为是零，是朝着阐明它们的本质和探索超越粒子物理学标准模型的新物理学迈出的重要一步。这个研究项目解决了这两个令人着迷的问题。回答这样的物理问题需要从大量的天文数据集中以足够的精度提取信息。这是一个复杂的问题，因为当一个人将模型从标准模型中改变出来时，对可观测物的改变是非常微妙的。需要先进的技术来梳理物理。然而，现有的方法依赖于各种简化和假设，在某种程度上是站不住脚的。对于下一代的星系调查，改进这些将是至关重要的。作为回应，我的技术代表了对如何分析数据的彻底反思，将物理定律和仪器工作纳入数据分析中使用的计算机模型。多亏了我在最近几年引入的新方法，这才有可能做到，包括统计算法，它大大减少了所需的模型评估数量，以及计算物理技术，使这些评估大规模并行。这些发展形成了我将遵循的原则性方法论的基础，即“基于模拟的推理”。使用物理计算机模型的基于模拟的推理将使我第一次能够利用宇宙大尺度结构的完整观测图，从而超越所谓的“相关函数”的标准估计器所获得的结果。因此，使我能够实现科学目标的关键创新是同时分析在大规模结构调查中可观察到的两种主要效应：星系群集和弱引力透镜。这一发展在欧几里得卫星大规模观测项目的背景下尤为及时和重要，因为它将为其核心方案的两个方面提供独特的见解。利用物理计算机模型联合分析星系团和弱引力透镜将减少统计和系统的不确定性，提高宇宙学结果的精度，并提高基于相关函数的标准方法的精度。我的建议包括开发、验证和应用新的物理计算机数据模型，用于星系调查数据分析，以解决这两个科学目标。这项研究构成了从大规模天文数据中提取物理信息的概念上的全新方法。提出的工作将最终通过基于模拟的推理对合成欧几里德数据进行第一次联合聚类透镜分析，然后是真实欧几里德数据。这些分析将产生具有参考质量的宇宙学信息，由以下成果提供：星系群集和弱引力透镜对加速膨胀的联合约束，以及对中微子质量的约束。该奖学金将在帝国推理和宇宙学中心举办。

Physical cosmology is dominated by a successful standard model connecting high-energy physics to observational astronomy, in which the Universe evolves from the "Big Bang" to its present state. However, we currently do not know the cause of the accelerated expansion and the masses of neutrino particles. Precise measurements of the acceleration of expansion will test the validity of general relativity and shed light on the physical nature of dark energy, an unknown form of energy that affects the Universe on the largest scales. Measuring the unknown masses of neutrinos, long thought to be zero, is an important step towards elucidating their essential nature and exploring new physics beyond the standard model of particle physics. This research programme tackles these two enthralling problems.Answering such physical questions requires extracting information from large astronomical data sets with sufficient accuracy. This is a complex problem, since changes to observables when one changes the model away from the standard model are extremely subtle. Advanced techniques are required to tease out the physics. However, existing methods rely on various simplifications and assumptions that at some level do not hold. For the next generation of galaxy surveys, it will be vital to improve upon these.As a response, my techniques represent a radical rethinking of how to analyse data, incorporating of the laws of physics and the working of instruments into computer models used within data analysis. This has only been possible to do at all in the last few years thanks to novel methods that I introduced, including statistical algorithms that drastically reduce the number of model evaluations required, and computational physics techniques that make these evaluations massively parallel. These developments form the basis of the principled methodology that I will follow, known as 'simulation-based inference'.Simulation-based inference using physical computer models will uniquely enable me, for the first time, to exploit complete observed maps of the large-scale structure of the Universe and, thus, to go beyond the results obtained with standard estimators of so-called 'correlation functions'. Consequently, the key innovation that will allow me to address the science goals is a simultaneous analysis of the two main effects observable in large-scale structure surveys: galaxy clustering and weak gravitational lensing. This development is particularly timely and significant in the context of the large-scale observational project of ESA's Euclid satellite, as it will provide unique insights into both aspects of its core programme. The use of physical computer models to jointly analyse galaxy clustering and weak gravitational lensing will reduce statistical and systematic uncertainties, increase the precision of cosmological results, and improve their accuracy with respect to standard methods based on correlation functions.My proposal consists of developing, validating, and applying new physical computer data models for galaxy survey data analysis, in order to address the two scientific objectives. This research constitutes a conceptually entirely new approach to extracting physical information from large-scale astronomical data. The proposed work will culminate with the first joint clustering-lensing analyses of synthetic, then real Euclid data via simulation-based inference. These analyses will produce cosmological information of reference quality, provided by the following deliverables: joint constraints from galaxy clustering and weak gravitational lensing on the acceleration of the expansion, and on neutrino masses.The fellowship will be hosted at the Imperial Centre for Inference and Cosmology.